From the Editor
“Compared with treatment of physical conditions, the quality of care of mental health disorders remains poor, and the rate of improvement in treatment is slow. Outcomes for many mental disorders have stagnated or even declined since the original treatments were developed.”
Are there two sentences more disappointing to read? One in five Canadians will experience a mental health problem this year – and yet we have basic problems with quality (and access).
Could AI and machine learning help?
In the first selection, we consider a new JAMA Psychiatry paper which opens with the two sentences above. The University of Cambridge’s Michael P. Ewbankand his co-authors don’t simply bemoan the status quo but seek to change it – they “developed a method of objectively quantifying psychotherapy using a deep learning approach to automatically categorize therapist utterances from approximately 90 000 hours of [internet-delivered CBT]…” In other words, by breaking therapy down into a couple of dozen techniques and then employing machine learning, they attempt to match techniques with outcomes (patient improvement and engagement), with an eye on finding what works and what doesn’t. And, yes, you read that right: they drew on 90 000 hours of therapy. They show: “factors specific to CBT, as well as factors common to most psychotherapies, are associated with increased odds of reliable improvement in patient symptoms.”
Can computers (and machine learning) improve human therapy?
In the second selection, we consider the comments of University of British Columbia President Santa Ono about school and the stresses of school. Ono speaks about his own struggle with depression. “I’ve been there at the abyss.”